import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.metrics import accuracy_score, classification_report
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import GradientBoostingClassifier
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.neural_network import MLPClassifier

URL = 'heart_failure_clinical_records_dataset_14_columns.csv'
df = pd.read_csv(URL)

# Dataset has 11 features (numerical and categorical) and 1 Target (categorical), which is the presence of a heart disease.
df.head()

# Description of numerical features
df.describe().T

df.shape

(303, 14)

df

# Dataset has 13 features (numerical and categorical) and 1 Target (categorical).
df.dtypes

age           int64
sex           int64
cp            int64
trtbps        int64
chol          int64
fbs           int64
restecg       int64
thalachh      int64
exng          int64
oldpeak     float64
slp           int64
caa           int64
thall         int64
output        int64
dtype: object

# Display the first few rows of the dataframe
display(df.head(10))

print(30 * "-", "COLUMNS", 30 * "-")
print(df.columns, "\n", 30 * "-","INFO",30 * "-")

display(df.info())

------------------------------ COLUMNS ------------------------------
Index(['age', 'sex', 'cp', 'trtbps', 'chol', 'fbs', 'restecg', 'thalachh',
       'exng', 'oldpeak', 'slp', 'caa', 'thall', 'output'],
      dtype='object') 
 ------------------------------ INFO ------------------------------
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 303 entries, 0 to 302
Data columns (total 14 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   age       303 non-null    int64  
 1   sex       303 non-null    int64  
 2   cp        303 non-null    int64  
 3   trtbps    303 non-null    int64  
 4   chol      303 non-null    int64  
 5   fbs       303 non-null    int64  
 6   restecg   303 non-null    int64  
 7   thalachh  303 non-null    int64  
 8   exng      303 non-null    int64  
 9   oldpeak   303 non-null    float64
 10  slp       303 non-null    int64  
 11  caa       303 non-null    int64  
 12  thall     303 non-null    int64  
 13  output    303 non-null    int64  
dtypes: float64(1), int64(13)
memory usage: 33.3 KB

None

# Check for missing & dublicate values
print(f"number of null values: {df.isnull().sum().sum()}")
print(f"number of dublicate values: {df.duplicated().sum()}")

number of null values: 0
number of dublicate values: 1

string_col = df.select_dtypes(include = "object").columns
df[string_col] = df[string_col].astype("string")

df.dtypes

age           int64
sex           int64
cp            int64
trtbps        int64
chol          int64
fbs           int64
restecg       int64
thalachh      int64
exng          int64
oldpeak     float64
slp           int64
caa           int64
thall         int64
output        int64
dtype: object

df.shape

(303, 14)

# Explore the distribution of the target variable
sns.countplot(data = df, x = 'output', color = 'navy')
plt.title('Distribution of Heart Attack')
plt.show()

# Select only numeric columns
# Remove categorical features and keep numerical features only for correlation coefficients analysis.
# When creating a correlation coefficients plot, it is generally more meaningful to correlate only the numerical features. Correlation coefficients measure the linear relationship between two continuous variables, so applying them to categorical features does not provide useful information.
numeric_df = df.select_dtypes(include = [float, int])

# Calculate the correlation matrix
corr_matrix = numeric_df.corr()

# Set the figure size
plt.figure(figsize = (12, 10))

# Set the diagonal values to NaN
np.fill_diagonal(corr_matrix.values, np.nan)

# Create the heatmap with correlation values annotated and font size set to 10
sns.heatmap(abs(corr_matrix), annot = True, fmt = ".3f", cmap = "Greys", annot_kws = {"size": 8})

# Add the title
# plt.title("\n Correlation Plot of the Heart Failure Prediction \n")

# Display the plot
plt.show()

# Distribution of Heat Diseases with respect to male and female
import plotly.express as px

# Assuming df is your DataFrame
fig = px.histogram( df, 
                    x = "output",
                    color = "sex",
                    hover_data = df.columns,
                    title = "Distribution of Heart Diseases",
                    barmode = "group",
                    color_discrete_sequence = ["navy", "#EB0A1E"])  # Pastel Rose and Pastel Blue color codes

# Update the layout and margins to set the plot
fig.update_layout(  plot_bgcolor = 'white', width = 800, height = 600,
                    margin = dict(l = 20, r = 20, t = 40, b = 20))  # Adjust margins to make the plot tighter

fig.show()

import plotly.express as px

# Assuming df is your DataFrame
fig = px.histogram( df,
                    x = "sex",
                    hover_data = df.columns,
                    title = "Sex Ratio in the Data",
                    color_discrete_sequence = ["navy", "red"])

# Update the layout and margins to set the plot
fig.update_layout(  plot_bgcolor = 'white', width = 800, height = 600,
                    margin = dict(l = 20, r = 20, t = 40, b = 20), 
                    bargap = 0.2)

fig.show()

import matplotlib.pyplot as plt
import seaborn as sns

# Set the background to white
sns.set_style("white")

# Define the color palette
palette = ["#122746", "#EB0A1E"]
# palette = ["navy", "red"]

# plt.figure(figsize = (15, 10))

# Identify categorical features, excluding target feature 'output'
binary_features = df.columns[(df.nunique() <= 5) | (df.isin([0, 1]).all())]
binary_features = binary_features.drop('output')
# Remove categorical features from the DataFrame, except 'output'
df_filtered = df.drop(columns = binary_features)

# Remove instances where the value is 0 for the remaining numerical features, excluding 'output'
# null_instances = df_filtered.drop(columns = ['output'])
# df_filtered = df_filtered[(null_instances != 0).all(axis = 1)]



# Create the pair plot
sns.pairplot(df_filtered, hue = "output", palette = palette)

# plt.tight_layout()

plt.show()

import matplotlib.pyplot as plt
import seaborn as sns

plt.figure(figsize = (15, 8))

for i, col in enumerate(df.columns, 1):
    plt.subplot(4, 4, i)
    plt.title(f"Distribution of {col} Data")
    sns.histplot(df[col], kde = True, color = 'navy')
    plt.tight_layout()

plt.show()

# Distribution of Age

import plotly.express as px

# Assuming df is your DataFrame
fig = px.box(   df, 
                y = "age", 
                x = "output", 
                title = "Distribution of Age",
                color_discrete_sequence = ["navy"])

# Update the layout and margins to set the plot
fig.update_layout(  plot_bgcolor = 'white', width = 800, height = 600,
                    margin = dict(l = 20, r = 20, t = 40, b = 20))

fig.show()

# Distribution of cholesterol

import plotly.express as px

# Assuming df is your DataFrame
fig = px.box(   df, 
                y = "chol", 
                x = "output", 
                title = "Distribution of Cholesterol",
                color_discrete_sequence = ["navy"])

# Update the layout and margins to set the plot
fig.update_layout(  plot_bgcolor = 'white', width = 800, height = 600,
                    margin = dict(l = 20, r = 20, t = 40, b = 20))

fig.show()

# Distribution of ST Depression

import plotly.express as px

# Assuming df is your DataFrame
fig = px.box(   df, 
                y = "oldpeak", 
                x = "output", 
                title = "Distribution of ST Depression",
                color_discrete_sequence = ["navy"])

# Update the layout and margins to set the plot
fig.update_layout(  plot_bgcolor = 'white', width = 800, height = 600,
                    margin = dict(l = 20, r = 20, t = 40, b = 20))

fig.show()

# Distribution of Bloodpressure

import plotly.express as px

# Assuming df is your DataFrame
fig = px.box(   df, 
                y = "trtbps", 
                x = "output", 
                title = "Distribution of Bloodpressure",
                color_discrete_sequence = ["navy"])

# Update the layout and margins to set the plot
fig.update_layout(  plot_bgcolor = 'white', width = 800, height = 600,
                    margin = dict(l = 20, r = 20, t = 40, b = 20))

fig.show()

# Distribution of Maximum Heart Rate

import plotly.express as px

# Assuming df is your DataFrame
fig = px.box(   df, 
                y = "trtbps", 
                x = "output", 
                title = "Distribution of Maximum Heart Rate",
                color_discrete_sequence = ["navy"])

# Update the layout and margins to set the plot
fig.update_layout(  plot_bgcolor = 'white', width = 800, height = 600,
                    margin = dict(l = 20, r = 20, t = 40, b = 20))

fig.show()

# Checking for Type of data
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 303 entries, 0 to 302
Data columns (total 14 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   age       303 non-null    int64  
 1   sex       303 non-null    int64  
 2   cp        303 non-null    int64  
 3   trtbps    303 non-null    int64  
 4   chol      303 non-null    int64  
 5   fbs       303 non-null    int64  
 6   restecg   303 non-null    int64  
 7   thalachh  303 non-null    int64  
 8   exng      303 non-null    int64  
 9   oldpeak   303 non-null    float64
 10  slp       303 non-null    int64  
 11  caa       303 non-null    int64  
 12  thall     303 non-null    int64  
 13  output    303 non-null    int64  
dtypes: float64(1), int64(13)
memory usage: 33.3 KB

# Checking for NULL data in the data set
df.isnull().sum()

age         0
sex         0
cp          0
trtbps      0
chol        0
fbs         0
restecg     0
thalachh    0
exng        0
oldpeak     0
slp         0
caa         0
thall       0
output      0
dtype: int64

from sklearn.preprocessing import LabelEncoder

# Convert 'object' type to 'string'
string_col = df.select_dtypes(include = "object").columns
df[string_col] = df[string_col].astype("string")

# Getting the categorical columns 
string_col = df.select_dtypes("string").columns.to_list()

df[string_col].head()

for col in string_col:
    print(f"Distribution of categorical values in the {col}: ")
    print(df[col].value_counts())

df.describe().T

# Split the data into features (X) and target variable (y)
X = df.drop('output', axis = 1)
y = df['output']

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 42)

# Handle missing values (if any)
imputer = SimpleImputer(strategy = 'mean')
X_train = imputer.fit_transform(X_train)
X_test = imputer.transform(X_test)

# Standardize features
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

models = {
    'Logistic Regression': LogisticRegression(),
    'Decision Tree': DecisionTreeClassifier(),
    'Random Forest': RandomForestClassifier(),
    'SVM': SVC(probability = True),
    'KNN': KNeighborsClassifier(),
    'Gradient Boosting': GradientBoostingClassifier(),
    'XGBoost': XGBClassifier(),
    'AdaBoost': AdaBoostClassifier(algorithm = 'SAMME'), 
    'Naive Bayes': GaussianNB(),       
    'MLP Neural Network': MLPClassifier(max_iter = 1000) 
}

for name, model in models.items():
    print(  f"**{name.upper()}** Training \n", 50 * "-")
    
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)

    accuracy = accuracy_score(y_test, y_pred)
    
    print(f"{name} Accuracy: {accuracy: 2.2f}")
    print(classification_report(y_test, y_pred))

**LOGISTIC REGRESSION** Training 
 --------------------------------------------------
Logistic Regression Accuracy:  0.85
              precision    recall  f1-score   support

           0       0.83      0.86      0.85        29
           1       0.87      0.84      0.86        32

    accuracy                           0.85        61
   macro avg       0.85      0.85      0.85        61
weighted avg       0.85      0.85      0.85        61

**DECISION TREE** Training 
 --------------------------------------------------
Decision Tree Accuracy:  0.85
              precision    recall  f1-score   support

           0       0.79      0.93      0.86        29
           1       0.93      0.78      0.85        32

    accuracy                           0.85        61
   macro avg       0.86      0.86      0.85        61
weighted avg       0.86      0.85      0.85        61

**RANDOM FOREST** Training 
 --------------------------------------------------
Random Forest Accuracy:  0.85
              precision    recall  f1-score   support

           0       0.86      0.83      0.84        29
           1       0.85      0.88      0.86        32

    accuracy                           0.85        61
   macro avg       0.85      0.85      0.85        61
weighted avg       0.85      0.85      0.85        61

**SVM** Training 
 --------------------------------------------------
SVM Accuracy:  0.87
              precision    recall  f1-score   support

           0       0.84      0.90      0.87        29
           1       0.90      0.84      0.87        32

    accuracy                           0.87        61
   macro avg       0.87      0.87      0.87        61
weighted avg       0.87      0.87      0.87        61

**KNN** Training 
 --------------------------------------------------
KNN Accuracy:  0.90
              precision    recall  f1-score   support

           0       0.87      0.93      0.90        29
           1       0.93      0.88      0.90        32

    accuracy                           0.90        61
   macro avg       0.90      0.90      0.90        61
weighted avg       0.90      0.90      0.90        61

**GRADIENT BOOSTING** Training 
 --------------------------------------------------
Gradient Boosting Accuracy:  0.79
              precision    recall  f1-score   support

           0       0.77      0.79      0.78        29
           1       0.81      0.78      0.79        32

    accuracy                           0.79        61
   macro avg       0.79      0.79      0.79        61
weighted avg       0.79      0.79      0.79        61

**XGBOOST** Training 
 --------------------------------------------------
XGBoost Accuracy:  0.82
              precision    recall  f1-score   support

           0       0.78      0.86      0.82        29
           1       0.86      0.78      0.82        32

    accuracy                           0.82        61
   macro avg       0.82      0.82      0.82        61
weighted avg       0.82      0.82      0.82        61

**ADABOOST** Training 
 --------------------------------------------------
AdaBoost Accuracy:  0.82
              precision    recall  f1-score   support

           0       0.76      0.90      0.83        29
           1       0.89      0.75      0.81        32

    accuracy                           0.82        61
   macro avg       0.83      0.82      0.82        61
weighted avg       0.83      0.82      0.82        61

**NAIVE BAYES** Training 
 --------------------------------------------------
Naive Bayes Accuracy:  0.87
              precision    recall  f1-score   support

           0       0.84      0.90      0.87        29
           1       0.90      0.84      0.87        32

    accuracy                           0.87        61
   macro avg       0.87      0.87      0.87        61
weighted avg       0.87      0.87      0.87        61

**MLP NEURAL NETWORK** Training 
 --------------------------------------------------
MLP Neural Network Accuracy:  0.84
              precision    recall  f1-score   support

           0       0.81      0.86      0.83        29
           1       0.87      0.81      0.84        32

    accuracy                           0.84        61
   macro avg       0.84      0.84      0.84        61
weighted avg       0.84      0.84      0.84        61

# Initialize the accuracies dictionary
accuracies = {}

for name, model in models.items():
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)
    accuracies[name] = accuracy
    print(f"{name} Accuracy: {accuracy:.3f} \n", 40*'-')

# Create a bar plot for accuracies
plt.figure(figsize = (10, 6))
plt.barh(list(accuracies.keys()), list(accuracies.values()), color = 'blue')
plt.xlabel('Accuracy')
plt.title('Accuracy of Different Models')
plt.xlim(0, 1)
plt.gca().invert_yaxis() 
plt.show()

Logistic Regression Accuracy: 0.852 
 ----------------------------------------
Decision Tree Accuracy: 0.820 
 ----------------------------------------
Random Forest Accuracy: 0.869 
 ----------------------------------------
SVM Accuracy: 0.869 
 ----------------------------------------
KNN Accuracy: 0.902 
 ----------------------------------------
Gradient Boosting Accuracy: 0.787 
 ----------------------------------------
XGBoost Accuracy: 0.820 
 ----------------------------------------
AdaBoost Accuracy: 0.820 
 ----------------------------------------
Naive Bayes Accuracy: 0.869 
 ----------------------------------------
MLP Neural Network Accuracy: 0.836 
 ----------------------------------------

'''
import pandas as pd

# Assuming you have a trained Random Forest model
importances = model.feature_importances_
feature_names = df.columns[:-1]  # Exclude the target column
feature_importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': importances})
feature_importance_df = feature_importance_df.sort_values(by = 'Importance', ascending  False)

print(feature_importance_df)
'''

"\nimport pandas as pd\n\n# Assuming you have a trained Random Forest model\nimportances = model.feature_importances_\nfeature_names = df.columns[:-1]  # Exclude the target column\nfeature_importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': importances})\nfeature_importance_df = feature_importance_df.sort_values(by = 'Importance', ascending  False)\n\nprint(feature_importance_df)\n"

tree_based_models = ['Decision Tree', 'Random Forest', 'Gradient Boosting', 'XGBoost', 'AdaBoost']
for name, model in models.items():
    if name in tree_based_models:
        model.fit(X_train, y_train)
        importances = model.feature_importances_
        feature_names = df.columns[:-1]  # Exclude the target column
        feature_importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': importances})
        feature_importance_df = feature_importance_df.sort_values(by = 'Importance', ascending = False)
        print(f"{name} Feature Importance:\n", feature_importance_df)

Decision Tree Feature Importance:
      Feature  Importance
2         cp    0.225263
11       caa    0.133084
9    oldpeak    0.111877
0        age    0.109239
4       chol    0.076986
8       exng    0.075421
3     trtbps    0.072832
7   thalachh    0.061768
1        sex    0.041015
10       slp    0.039962
12     thall    0.036456
6    restecg    0.016097
5        fbs    0.000000
Random Forest Feature Importance:
      Feature  Importance
11       caa    0.133282
9    oldpeak    0.123808
2         cp    0.105023
12     thall    0.102163
0        age    0.094724
7   thalachh    0.094652
4       chol    0.080462
8       exng    0.077078
3     trtbps    0.075344
10       slp    0.047091
1        sex    0.037797
6    restecg    0.019198
5        fbs    0.009380
Gradient Boosting Feature Importance:
      Feature  Importance
11       caa    0.175155
2         cp    0.171475
12     thall    0.143273
9    oldpeak    0.116469
0        age    0.091597
10       slp    0.058723
4       chol    0.056656
8       exng    0.055651
3     trtbps    0.041160
1        sex    0.039280
7   thalachh    0.038052
5        fbs    0.007390
6    restecg    0.005118
XGBoost Feature Importance:
      Feature  Importance
2         cp    0.210669
12     thall    0.154961
8       exng    0.143586
11       caa    0.122759
9    oldpeak    0.085633
1        sex    0.063141
10       slp    0.051550
0        age    0.033977
7   thalachh    0.032677
3     trtbps    0.032608
4       chol    0.029840
6    restecg    0.024334
5        fbs    0.014263
AdaBoost Feature Importance:
      Feature  Importance
9    oldpeak    0.197796
4       chol    0.143709
11       caa    0.137496
0        age    0.097600
7   thalachh    0.096432
2         cp    0.068981
1        sex    0.066484
12     thall    0.058450
10       slp    0.055191
3     trtbps    0.039632
8       exng    0.038229
5        fbs    0.000000
6    restecg    0.000000

'''
coefficients = model.coef_[0]
feature_importance_df = pd.DataFrame({'Feature': feature_names, 'Coefficient': coefficients})
feature_importance_df = feature_importance_df.sort_values(by = 'Coefficient', ascending = False)

print(feature_importance_df)
'''

"\ncoefficients = model.coef_[0]\nfeature_importance_df = pd.DataFrame({'Feature': feature_names, 'Coefficient': coefficients})\nfeature_importance_df = feature_importance_df.sort_values(by = 'Coefficient', ascending = False)\n\nprint(feature_importance_df)\n"

linear_models = ['Logistic Regression']
for name, model in models.items():
    if name in linear_models:
        model.fit(X_train, y_train)
        coefficients = model.coef_[0]
        feature_names = df.columns[:-1]  # Exclude the target column
        feature_importance_df = pd.DataFrame({'Feature': feature_names, 'Coefficient': coefficients})
        feature_importance_df = feature_importance_df.sort_values(by='Coefficient', ascending=False)
        print(f"{name} Coefficients:\n", feature_importance_df)

Logistic Regression Coefficients:
      Feature  Coefficient
2         cp     0.817904
10       slp     0.435302
7   thalachh     0.395131
6    restecg     0.284179
5        fbs     0.090312
0        age    -0.085591
4       chol    -0.174841
3     trtbps    -0.273307
8       exng    -0.517584
12     thall    -0.565659
9    oldpeak    -0.681396
1        sex    -0.783086
11       caa    -0.789955

'''
from sklearn.inspection import permutation_importance

result = permutation_importance(model, X_test, y_test, n_repeats = 10, random_state = 42)
perm_importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': result.importances_mean})
perm_importance_df = perm_importance_df.sort_values(by = 'Importance', ascending = False)

print(perm_importance_df)
'''

"\nfrom sklearn.inspection import permutation_importance\n\nresult = permutation_importance(model, X_test, y_test, n_repeats = 10, random_state = 42)\nperm_importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': result.importances_mean})\nperm_importance_df = perm_importance_df.sort_values(by = 'Importance', ascending = False)\n\nprint(perm_importance_df)\n"

from sklearn.inspection import permutation_importance

for name, model in models.items():
    model.fit(X_train, y_train)
    result = permutation_importance(model, X_test, y_test, n_repeats = 10, random_state = 42)
    feature_names = df.columns[:-1]  # Exclude the target column
    perm_importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': result.importances_mean})
    perm_importance_df = perm_importance_df.sort_values(by = 'Importance', ascending = False)
    print(f"\n {name.upper()} Permutation Importance: \n", 50 * "-", "\n", perm_importance_df)

 LOGISTIC REGRESSION Permutation Importance: 
 -------------------------------------------------- 
      Feature    Importance
2         cp  5.081967e-02
1        sex  3.934426e-02
9    oldpeak  2.622951e-02
12     thall  2.131148e-02
11       caa  1.967213e-02
7   thalachh  1.967213e-02
3     trtbps  1.639344e-03
8       exng -2.220446e-17
0        age -3.330669e-17
4       chol -4.918033e-03
5        fbs -6.557377e-03
6    restecg -8.196721e-03
10       slp -9.836066e-03

 DECISION TREE Permutation Importance: 
 -------------------------------------------------- 
      Feature  Importance
0        age    0.104918
2         cp    0.104918
9    oldpeak    0.085246
11       caa    0.078689
12     thall    0.059016
4       chol    0.057377
10       slp    0.024590
3     trtbps    0.014754
7   thalachh    0.013115
1        sex    0.008197
6    restecg    0.006557
5        fbs    0.000000
8       exng   -0.011475

 RANDOM FOREST Permutation Importance: 
 -------------------------------------------------- 
      Feature    Importance
2         cp  7.540984e-02
11       caa  4.918033e-02
1        sex  2.950820e-02
12     thall  2.622951e-02
9    oldpeak  1.639344e-02
7   thalachh  1.475410e-02
10       slp  1.475410e-02
8       exng  3.278689e-03
5        fbs  0.000000e+00
4       chol -2.220446e-17
0        age -3.278689e-03
6    restecg -6.557377e-03
3     trtbps -1.639344e-02

 SVM Permutation Importance: 
 -------------------------------------------------- 
      Feature    Importance
2         cp  7.540984e-02
12     thall  5.409836e-02
11       caa  4.918033e-02
9    oldpeak  2.786885e-02
1        sex  2.131148e-02
7   thalachh  1.967213e-02
4       chol  1.639344e-03
8       exng  0.000000e+00
10       slp -2.220446e-17
5        fbs -3.278689e-03
0        age -8.196721e-03
3     trtbps -8.196721e-03
6    restecg -3.114754e-02

 KNN Permutation Importance: 
 -------------------------------------------------- 
      Feature  Importance
12     thall    0.077049
1        sex    0.062295
11       caa    0.060656
2         cp    0.060656
7   thalachh    0.050820
9    oldpeak    0.045902
5        fbs    0.042623
0        age    0.034426
8       exng    0.031148
6    restecg    0.024590
3     trtbps    0.022951
10       slp    0.016393
4       chol    0.003279

 GRADIENT BOOSTING Permutation Importance: 
 -------------------------------------------------- 
      Feature  Importance
7   thalachh    0.037705
2         cp    0.034426
9    oldpeak    0.027869
1        sex    0.022951
3     trtbps    0.021311
12     thall    0.009836
10       slp    0.008197
11       caa    0.004918
6    restecg    0.000000
5        fbs   -0.001639
4       chol   -0.003279
8       exng   -0.003279
0        age   -0.014754

 XGBOOST Permutation Importance: 
 -------------------------------------------------- 
      Feature  Importance
1        sex    0.044262
11       caa    0.036066
2         cp    0.032787
7   thalachh    0.026230
12     thall    0.026230
6    restecg    0.019672
4       chol    0.016393
9    oldpeak    0.014754
10       slp    0.009836
0        age    0.008197
5        fbs    0.000000
8       exng   -0.006557
3     trtbps   -0.011475

 ADABOOST Permutation Importance: 
 -------------------------------------------------- 
      Feature  Importance
2         cp    0.044262
7   thalachh    0.039344
1        sex    0.032787
12     thall    0.031148
9    oldpeak    0.022951
8       exng    0.014754
11       caa    0.011475
10       slp    0.003279
5        fbs    0.000000
6    restecg    0.000000
3     trtbps   -0.003279
4       chol   -0.008197
0        age   -0.014754

 NAIVE BAYES Permutation Importance: 
 -------------------------------------------------- 
      Feature  Importance
9    oldpeak    0.068852
7   thalachh    0.067213
2         cp    0.055738
12     thall    0.054098
11       caa    0.040984
0        age    0.032787
8       exng    0.029508
1        sex    0.018033
10       slp    0.018033
3     trtbps    0.009836
4       chol    0.000000
5        fbs    0.000000
6    restecg    0.000000

 MLP NEURAL NETWORK Permutation Importance: 
 -------------------------------------------------- 
      Feature  Importance
12     thall    0.057377
11       caa    0.054098
2         cp    0.044262
1        sex    0.040984
7   thalachh    0.024590
6    restecg    0.021311
5        fbs    0.013115
0        age    0.009836
4       chol    0.003279
8       exng    0.001639
10       slp   -0.001639
9    oldpeak   -0.003279
3     trtbps   -0.008197

# pip install shap

# pip install tensorflow

'''
import shap

explainer = shap.Explainer(model, X_train)
shap_values = explainer(X_test)
shap.summary_plot(shap_values, X_test, feature_names = feature_names)
'''

'\nimport shap\n\nexplainer = shap.Explainer(model, X_train)\nshap_values = explainer(X_test)\nshap.summary_plot(shap_values, X_test, feature_names = feature_names)\n'

import shap

# Summarize the background data using k-means clustering
from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters = 10, random_state = 42).fit(X_train)
background = kmeans.cluster_centers_

for name, model in models.items():
    model.fit(X_train, y_train)

    if name in ['Decision Tree', 'Random Forest']:
        print(f"{name.upper()} SHAP Summary Plot \n", 50 * "-")
        print(f"Refer to Permutation Importance for analysis. \n")
    else:
        if name in ['Decision Tree', 'Random Forest', 'Gradient Boosting', 'XGBoost']:
            explainer = shap.TreeExplainer(model, background, feature_perturbation = 'interventional')
            shap_values = explainer(X_test, check_additivity = False)  # Disable additivity check
        
        elif name == 'Logistic Regression':
            explainer = shap.LinearExplainer(model, background)
            shap_values = explainer(X_test)
        
        elif name in ['SVM', 'KNN', 'AdaBoost', 'Naive Bayes', 'MLP Neural Network']:
            explainer = shap.KernelExplainer(model.predict, background)
            shap_values = explainer.shap_values(X_test)
        
        else:
            explainer = shap.Explainer(model, background)
            shap_values = explainer(X_test)
        

        feature_names = df.columns[:-1]  # Exclude the target column
        
        print(f"{name.upper()} SHAP Summary Plot \n", 50 * "-")
        shap.summary_plot(shap_values, X_test, feature_names = feature_names)

LOGISTIC REGRESSION SHAP Summary Plot 
 --------------------------------------------------

DECISION TREE SHAP Summary Plot 
 --------------------------------------------------
Refer to Permutation Importance for analysis. 

RANDOM FOREST SHAP Summary Plot 
 --------------------------------------------------
Refer to Permutation Importance for analysis.

  0%|          | 0/61 [00:00<?, ?it/s]

SVM SHAP Summary Plot 
 --------------------------------------------------

  0%|          | 0/61 [00:00<?, ?it/s]

KNN SHAP Summary Plot 
 --------------------------------------------------

GRADIENT BOOSTING SHAP Summary Plot 
 --------------------------------------------------

XGBOOST SHAP Summary Plot 
 --------------------------------------------------

  0%|          | 0/61 [00:00<?, ?it/s]

ADABOOST SHAP Summary Plot 
 --------------------------------------------------

'''
import lime
import lime.lime_tabular

explainer = lime.lime_tabular.LimeTabularExplainer(X_train.values, feature_names = feature_names, class_names = ['0', '1'], mode = 'classification')
i = 0  # Index of the instance to explain
exp = explainer.explain_instance(X_test.values[i], model.predict_proba, num_features = 13)
exp.show_in_notebook(show_table = True)
'''

"\nimport lime\nimport lime.lime_tabular\n\nexplainer = lime.lime_tabular.LimeTabularExplainer(X_train.values, feature_names = feature_names, class_names = ['0', '1'], mode = 'classification')\ni = 0  # Index of the instance to explain\nexp = explainer.explain_instance(X_test.values[i], model.predict_proba, num_features = 13)\nexp.show_in_notebook(show_table = True)\n"

# pip install lime

import lime
import lime.lime_tabular

for name, model in models.items():
    model.fit(X_train, y_train)
    explainer = lime.lime_tabular.LimeTabularExplainer(X_train, feature_names = feature_names, class_names = ['0', '1'], mode = 'classification')
    i = 0  # Index of the instance to explain
    exp = explainer.explain_instance(X_test[i], model.predict_proba, num_features = 13)
    print(f"{name} LIME Explanation for instance {i}:")
    exp.show_in_notebook(show_table = True)

Logistic Regression LIME Explanation for instance 0:

Decision Tree LIME Explanation for instance 0:

Random Forest LIME Explanation for instance 0:

SVM LIME Explanation for instance 0:

KNN LIME Explanation for instance 0:

Gradient Boosting LIME Explanation for instance 0:

XGBoost LIME Explanation for instance 0:

AdaBoost LIME Explanation for instance 0:

Naive Bayes LIME Explanation for instance 0:

MLP Neural Network LIME Explanation for instance 0:

	count	mean	std	min	25%	50%	75%	max
age	303.0	54.366337	9.082101	29.0	47.5	55.0	61.0	77.0
sex	303.0	0.683168	0.466011	0.0	0.0	1.0	1.0	1.0
cp	303.0	0.966997	1.032052	0.0	0.0	1.0	2.0	3.0
trtbps	303.0	131.623762	17.538143	94.0	120.0	130.0	140.0	200.0
chol	303.0	246.264026	51.830751	126.0	211.0	240.0	274.5	564.0
fbs	303.0	0.148515	0.356198	0.0	0.0	0.0	0.0	1.0
restecg	303.0	0.528053	0.525860	0.0	0.0	1.0	1.0	2.0
thalachh	303.0	149.646865	22.905161	71.0	133.5	153.0	166.0	202.0
exng	303.0	0.326733	0.469794	0.0	0.0	0.0	1.0	1.0
oldpeak	303.0	1.039604	1.161075	0.0	0.0	0.8	1.6	6.2
slp	303.0	1.399340	0.616226	0.0	1.0	1.0	2.0	2.0
caa	303.0	0.729373	1.022606	0.0	0.0	0.0	1.0	4.0
thall	303.0	2.313531	0.612277	0.0	2.0	2.0	3.0	3.0
output	303.0	0.544554	0.498835	0.0	0.0	1.0	1.0	1.0

	age	sex	cp	trtbps	chol	fbs	restecg	thalachh	exng	oldpeak	slp	caa	thall	output
0	63	1	3	145	233	1	0	150	0	2.3	0	0	1	1
1	37	1	2	130	250	0	1	187	0	3.5	0	0	2	1
2	41	0	1	130	204	0	0	172	0	1.4	2	0	2	1
3	56	1	1	120	236	0	1	178	0	0.8	2	0	2	1
4	57	0	0	120	354	0	1	163	1	0.6	2	0	2	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
298	57	0	0	140	241	0	1	123	1	0.2	1	0	3	0
299	45	1	3	110	264	0	1	132	0	1.2	1	0	3	0
300	68	1	0	144	193	1	1	141	0	3.4	1	2	3	0
301	57	1	0	130	131	0	1	115	1	1.2	1	1	3	0
302	57	0	1	130	236	0	0	174	0	0.0	1	1	2	0

	count	mean	std	min	25%	50%	75%	max
age	303.0	54.366337	9.082101	29.0	47.5	55.0	61.0	77.0
sex	303.0	0.683168	0.466011	0.0	0.0	1.0	1.0	1.0
cp	303.0	0.966997	1.032052	0.0	0.0	1.0	2.0	3.0
trtbps	303.0	131.623762	17.538143	94.0	120.0	130.0	140.0	200.0
chol	303.0	246.264026	51.830751	126.0	211.0	240.0	274.5	564.0
fbs	303.0	0.148515	0.356198	0.0	0.0	0.0	0.0	1.0
restecg	303.0	0.528053	0.525860	0.0	0.0	1.0	1.0	2.0
thalachh	303.0	149.646865	22.905161	71.0	133.5	153.0	166.0	202.0
exng	303.0	0.326733	0.469794	0.0	0.0	0.0	1.0	1.0
oldpeak	303.0	1.039604	1.161075	0.0	0.0	0.8	1.6	6.2
slp	303.0	1.399340	0.616226	0.0	1.0	1.0	2.0	2.0
caa	303.0	0.729373	1.022606	0.0	0.0	0.0	1.0	4.0
thall	303.0	2.313531	0.612277	0.0	2.0	2.0	3.0	3.0
output	303.0	0.544554	0.498835	0.0	0.0	1.0	1.0	1.0

Feature	Description
Age	age of the patient [years]
Sex	sex of the patient [M: Male, F: Female]
ChestPainType	chest pain type [TA: Typical Angina, ATA: Atypical Angina, NAP: Non-Anginal Pain, ASY: Asymptomatic]
RestingBP	resting blood pressure [mm Hg]
Cholesterol	serum cholesterol [mm/dl]
FastingBS	fasting blood sugar [1: if FastingBS > 120 mg/dl, 0: otherwise]
RestingECG	resting electrocardiogram results [Normal: Normal, ST: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV), LVH: showing probable or definite left ventricular hypertrophy by Estes' criteria]
MaxHR	maximum heart rate achieved [Numeric value between 60 and 202]
ExerciseAngina	exercise-induced angina [Y: Yes, N: No]
Oldpeak	oldpeak = ST [Numeric value measured in depression]
ST_Slope	the slope of the peak exercise ST segment [Up: upsloping, Flat: flat, Down: downsloping]

	age	sex	cp	trtbps	chol	fbs	restecg	thalachh	exng	oldpeak	slp	thall	output
0	63	1	3	145	233	1	0	150	0	2.3	0	1	1
1	37	1	2	130	250	0	1	187	0	3.5	0	2	1
2	41	0	1	130	204	0	0	172	0	1.4	2	2	1
3	56	1	1	120	236	0	1	178	0	0.8	2	2	1
4	57	0	0	120	354	0	1	163	1	0.6	2	2	1

	age	sex	cp	trtbps	chol	fbs	restecg	thalachh	exng	oldpeak	slp	caa	thall	output
0	63	1	3	145	233	1	0	150	0	2.3	0	0	1	1
1	37	1	2	130	250	0	1	187	0	3.5	0	0	2	1
2	41	0	1	130	204	0	0	172	0	1.4	2	0	2	1
3	56	1	1	120	236	0	1	178	0	0.8	2	0	2	1
4	57	0	0	120	354	0	1	163	1	0.6	2	0	2	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
298	57	0	0	140	241	0	1	123	1	0.2	1	0	3	0
299	45	1	3	110	264	0	1	132	0	1.2	1	0	3	0
300	68	1	0	144	193	1	1	141	0	3.4	1	2	3	0
301	57	1	0	130	131	0	1	115	1	1.2	1	1	3	0
302	57	0	1	130	236	0	0	174	0	0.0	1	1	2	0

	age	sex	cp	trtbps	chol	fbs	restecg	thalachh	exng	oldpeak	slp	thall	output
0	63	1	3	145	233	1	0	150	0	2.3	0	1	1
1	37	1	2	130	250	0	1	187	0	3.5	0	2	1
2	41	0	1	130	204	0	0	172	0	1.4	2	2	1
3	56	1	1	120	236	0	1	178	0	0.8	2	2	1
4	57	0	0	120	354	0	1	163	1	0.6	2	2	1
5	57	1	0	140	192	0	1	148	0	0.4	1	1	1
6	56	0	1	140	294	0	0	153	0	1.3	1	2	1
7	44	1	1	120	263	0	1	173	0	0.0	2	3	1
8	52	1	2	172	199	1	1	162	0	0.5	2	3	1
9	57	1	2	150	168	0	1	174	0	1.6	2	2	1

	age	sex	cp	trtbps	chol	fbs	restecg	thalachh	exng	oldpeak	slp	thall	output
0	63	1	3	145	233	1	0	150	0	2.3	0	1	1
1	37	1	2	130	250	0	1	187	0	3.5	0	2	1
2	41	0	1	130	204	0	0	172	0	1.4	2	2	1
3	56	1	1	120	236	0	1	178	0	0.8	2	2	1
4	57	0	0	120	354	0	1	163	1	0.6	2	2	1

	age	sex	cp	trtbps	chol	fbs	restecg	thalachh	exng	oldpeak	slp	caa	thall	output
0	63	1	3	145	233	1	0	150	0	2.3	0	0	1	1
1	37	1	2	130	250	0	1	187	0	3.5	0	0	2	1
2	41	0	1	130	204	0	0	172	0	1.4	2	0	2	1
3	56	1	1	120	236	0	1	178	0	0.8	2	0	2	1
4	57	0	0	120	354	0	1	163	1	0.6	2	0	2	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
298	57	0	0	140	241	0	1	123	1	0.2	1	0	3	0
299	45	1	3	110	264	0	1	132	0	1.2	1	0	3	0
300	68	1	0	144	193	1	1	141	0	3.4	1	2	3	0
301	57	1	0	130	131	0	1	115	1	1.2	1	1	3	0
302	57	0	1	130	236	0	0	174	0	0.0	1	1	2	0

	age	sex	cp	trtbps	chol	fbs	restecg	thalachh	exng	oldpeak	slp	thall	output
0	63	1	3	145	233	1	0	150	0	2.3	0	1	1
1	37	1	2	130	250	0	1	187	0	3.5	0	2	1
2	41	0	1	130	204	0	0	172	0	1.4	2	2	1
3	56	1	1	120	236	0	1	178	0	0.8	2	2	1
4	57	0	0	120	354	0	1	163	1	0.6	2	2	1
5	57	1	0	140	192	0	1	148	0	0.4	1	1	1
6	56	0	1	140	294	0	0	153	0	1.3	1	2	1
7	44	1	1	120	263	0	1	173	0	0.0	2	3	1
8	52	1	2	172	199	1	1	162	0	0.5	2	3	1
9	57	1	2	150	168	0	1	174	0	1.6	2	2	1

HEART DISEASE PREDICTION¶

Supervised Machine Learning: Classification¶

Import Libraries¶

CARDIOVASCULAR DISEASES¶

HEARTH FAILURE¶

Read Dataset¶

DATA SET FEATURES¶

Data Information¶

Missing & Duplicate Values¶

FEATURES¶

TARGET¶

EXPLORATORY DATA ANALYSIS (EDA)¶

Correlation Matrix¶

DATA SKEWNESS¶

DATA - BOXPLOT (OUTLIERS)¶

Data Preprocessing¶

Handling Null Values:¶

Handling Missing Values in Datasets¶

Imputation¶

2. Feature Scaling¶

Distance-Based Algorithms¶

Tree-Based Algorithms¶

Normalization¶

Standardization¶

Question – Normalize or Standardize?¶

Robust Scaler¶

3. Handling Categorical Variables¶

FEATURES¶

TARGET¶

EXPLORATORY DATA ANALYSIS (EDA)¶

MODEL BUILDING¶

Cross-Validation Techniques¶

A. K-fold cross-validation¶

B. Stratified k-fold cross-validation¶

Standardization¶

MACHINE LEARNING MODEL TRAINING¶

Logistic Regression¶

Decision Tree¶

Random Forest¶

Support Vector Machine (SVM)¶

K-Nearest Neighbors (KNN)¶

Gradient Boosting¶

XGBoost¶

AdaBoost¶

Naive Bayes¶

MODELS ACCURACY COMPARISSON¶

FEATURE IMPORTANCE¶

CONCLUSIONS¶

Improvement Points¶

	age	sex	cp	trtbps	chol	fbs	restecg	thalachh	exng	oldpeak	slp	thall	output
0	63	1	3	145	233	1	0	150	0	2.3	0	1	1
1	37	1	2	130	250	0	1	187	0	3.5	0	2	1
2	41	0	1	130	204	0	0	172	0	1.4	2	2	1
3	56	1	1	120	236	0	1	178	0	0.8	2	2	1
4	57	0	0	120	354	0	1	163	1	0.6	2	2	1

	age	sex	cp	trtbps	chol	fbs	restecg	thalachh	exng	oldpeak	slp	caa	thall	output
0	63	1	3	145	233	1	0	150	0	2.3	0	0	1	1
1	37	1	2	130	250	0	1	187	0	3.5	0	0	2	1
2	41	0	1	130	204	0	0	172	0	1.4	2	0	2	1
3	56	1	1	120	236	0	1	178	0	0.8	2	0	2	1
4	57	0	0	120	354	0	1	163	1	0.6	2	0	2	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
298	57	0	0	140	241	0	1	123	1	0.2	1	0	3	0
299	45	1	3	110	264	0	1	132	0	1.2	1	0	3	0
300	68	1	0	144	193	1	1	141	0	3.4	1	2	3	0
301	57	1	0	130	131	0	1	115	1	1.2	1	1	3	0
302	57	0	1	130	236	0	0	174	0	0.0	1	1	2	0

	age	sex	cp	trtbps	chol	fbs	restecg	thalachh	exng	oldpeak	slp	thall	output
0	63	1	3	145	233	1	0	150	0	2.3	0	1	1
1	37	1	2	130	250	0	1	187	0	3.5	0	2	1
2	41	0	1	130	204	0	0	172	0	1.4	2	2	1
3	56	1	1	120	236	0	1	178	0	0.8	2	2	1
4	57	0	0	120	354	0	1	163	1	0.6	2	2	1
5	57	1	0	140	192	0	1	148	0	0.4	1	1	1
6	56	0	1	140	294	0	0	153	0	1.3	1	2	1
7	44	1	1	120	263	0	1	173	0	0.0	2	3	1
8	52	1	2	172	199	1	1	162	0	0.5	2	3	1
9	57	1	2	150	168	0	1	174	0	1.6	2	2	1